code: def evaluations_to_columns(
      evaluation: Dict[str, Optional[OrderedDict]], decimals=10
  ) -> List[Tuple[str, Number]]:
      """Convert the results of :meth:`metrics.ScoringMixIn.evaluate` to a pd.DataFrame-ready format
  
      Parameters
      ----------
      evaluation: Dict[str, OrderedDict]
          The result of consecutive calls to :meth:`metrics.ScoringMixIn.evaluate` for all given
          dataset types
      decimals: Int, default=10
          Number of decimal places to which to round. If `decimals` is negative, it specifies the
          number of positions to the left of the decimal point
  
      Returns
      -------
      column_metrics: list of pairs
          A pair for each data_type-metric combination, where the first item is the key, and the
          second is the metric value
  
      Examples
      --------
      >>> evaluations_to_columns({
      ...     'in_fold': None,
      ...     'holdout': OrderedDict([('roc_auc_score', 0.9856), ('f1_score', 0.9768)]),
      ...     'oof': OrderedDict([('roc_auc_score', 0.9634)])
      ... })
      [('oof_roc_auc_score', 0.9634), ('holdout_roc_auc_score', 0.9856), ('holdout_f1_score', 0.9768)]
      """
      data_types = ["oof", "holdout", "in_fold"]
      column_metrics = []
  
      for data_type in data_types:
          if evaluation[data_type] is not None:
              for metric_key, metric_val in evaluation[
                  data_type
              ].items():
                  column_metrics.append(
                      (
                          f"{data_type}_{metric_key}",
                          np.round(metric_val, decimals),
                      )
                  )
  
      return column_metrics
  
file_path: hyperparameter_hunter/hyperparameter_hunter/i_o/leaderboards.py
imports: from abc import ABCMeta, abstractmethod
  from collections import OrderedDict
  from numbers import Number
  import numpy
  import pandas
  from typing import Dict, List, Optional, Tuple
label_desc: Use image node_id 1 for calling a global function with example usage: evaluations_to_columns(evaluation, decimals) and returns: column_metrics